Recent development and wide spread use of medical imaging apparatuses for Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) have made it possible to obtain a large volume of high-definition digital images for medical use. Furthermore, medical images already interpreted by doctors are increasingly accumulated one by one together with the image interpretation reports thereof in Picture Archiving and Communication Systems (PACS). In order to interpret a target image with reference to medical images similar to the target image, a start is made for development of techniques for searching out the similar images (medical images) from already-accumulated past cases.
In a similar image search, it is important to optimize image feature quantities for determining a similarity between images according to a current target image for the similar image search. Conventionally, image feature quantities are designed for each of target organs for which similar image searches are performed. In most of similar image searches, the same image feature quantities are used in common to concept levels other than such organs (examples of such concept levels include the kinds of diseases, the progress (stages) of the diseases or the seriousness of the diseases).
As a similar image search method for dynamically changing image feature quantities to be used in a similar image search according to concept levels other than an organ, the following technique is disclosed.
Non-patent Literature 1 proposes a searching approach composed of two steps that are “Customized-Queries” Approach (CQA) as a means for solving the problem. The first step of this approach is to classify query images using image feature quantities for classifying classes of the kinds of diseases, the progress of the diseases or the seriousness of the diseases, and the like in the optimum manner. The second step of this approach is to search similar images using the image feature quantities optimized for further classifying the cases included in each of the classes obtained as a result of the previous classification. At this time, the image feature quantities optimum for the classes are calculated in advance through unsupervised learning. Furthermore, the technique disclosed in the Non-patent Literature applies CQA for a lung CT images and to thereby achieve a search recall factor increased from those obtainable in such conventional similar image search using only a single kind of image feature quantities.